Experiment 1 demonstrated that binary classification is an improvement over heuristics, but is not scalable for inter-movie matching. A suitable approach to achieve this is metric learning, which maps a feature vector to a new embedding vector that can be directly searched through using nearest neighbor methods (rather than running a classifier for each pair). Here we take fixed shot encodings from Experiment 1, and learn a new embedding space via noise contrastive estimation [1]}.
We leverage the pytorch-metric-learning library [2]}, with NTXentLoss (a.k.a. InfoNCE) [3]}, [4]} and TripletMarginMiner with hard triplets and cosine similarity. The transformed embeddings can be indexed and retrieved with ANN methods as discussed in Sec. REF .
